Result: Automatised Pharmacophoric Deconvolution of Plant Extracts, application to Cinchona bark crude extract

Title:
Automatised Pharmacophoric Deconvolution of Plant Extracts, application to Cinchona bark crude extract
Contributors:
Laboratoire d'Innovation Thérapeutique (LIT), Université de Strasbourg (UNISTRA)-Institut de Chimie - CNRS Chimie (INC-CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Région Alsace, ANR-10-LABX-0034,Medalis,Medalis Drug Discovery Center(2010)
Source:
Faraday Discussions. :441-458
Publisher Information:
CCSD; Royal Society of Chemistry, 2019.
Publication Year:
2019
Collection:
collection:IN2P3
collection:DRS-IPHC
collection:CNRS
collection:IGBMC
collection:UNIV-STRASBG
collection:IPHC
collection:INC-CNRS
collection:SITE-ALSACE
collection:TEST-HALCNRS
collection:ANR
collection:TEST2-HALCNRS
collection:TEST3-HALCNRS
collection:TEST4-HALCNRS
collection:UNIVOAK
collection:LIT
Original Identifier:
HAL: hal-04751346
Document Type:
Journal article<br />Journal articles
Language:
English
ISSN:
1359-6640
1364-5498
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1039/C8FD00242H
DOI:
10.1039/C8FD00242H
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.04751346v1
Database:
HAL

Further Information

This method is based on the automatic acquisition of a standard set of NMR experiments from a medium size set of samples differing by their bioactivity. From this raw data, an analysis pipeline is run and the data is analysed by leveraging machine learning approaches in order to extract the spectral fingerprints of the active compounds. The optimal conditions for the analysis are determined, and tested on two different system, a synthetic sample where a single active molecule is to be isolated and characterized, and a complex bioactive matrix with synergetic interactions between the components. The method allows the identification of the active compounds and performs a pharmacophoric deconvolution. The program is made freely available on internet, with an interactive visualisation of the statistical analysis, at plasmodesma.igbmc.science.